Economics Teachers' Content Knowledge and Teaching Strategies Used to Teach Economics in Selected South African Schools
Bibliographic record
Abstract
Economics is a subject offered at the Further Education and Training (FET) section in South Africa, the subject has recorded performance that is not impressive, and the low enrolment and statistics of pass rate are of major concern. Therefore this paper explores the teachers’ content knowledge and strategies used to teach Economics in some selected schools in South Africa. The paper adopts a qualitative approach to phenomenological research design. Purposive sampling techniques were used to select 12 teachers from six schools, two teachers from each school in Buffalo City Municipality in East London, South Africa. A semi-structured interview was used to elicit information from the respondents. The findings among others revealed that Economics is very useful to be a better citizen and to make rational life decisions, the use of the right pedagogy can improve performance, and the content knowledge with adequate on-the-job training will be a match in delivering the content of the subject. It is concluded that a low level of understanding of the basic Economics concepts could be attributed to less professional development training of Economics teachers in content knowledge and pedagogy. It is recommended among others that the Economics teacher have to strike a balance between theory and practice. Teachers should be innovative and improvise by using technological skills, they should move towards the use of technology as a tool to enable learners to become creative, empathetic and high-order thinkers in this digital world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".